Deep Learning-Based Automatic Detection Model for Ocean Eddy in SAR Images

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  • 1.School of Remote Sensing& Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China;

    2. Laboratory for Regional Oceanography and Numerical Modeling, Qingdao Marine Science and Technology Center, 266200, Qingdao, Shandong Province, China;

    3. Innovation Center for Integrated Remote Sensing and Navigation Applications Engineering Technology, Ministry of Natural Resources, Nanjing, Jiangsu 210044, China;

    4. Jiangsu Collaborative Innovation Center for Precision Navigation and Intelligent Application Engineering, Nanjing, Jiangsu 210044, China

    5. School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China

Received date: 2024-12-29

  Revised date: 2025-02-18

  Accepted date: 2025-02-26

  Online published: 2025-02-26

Supported by

National Key R&D Program of China (2022YFC3104900/2022YFC3104905), National Natural Science Foundation of China (42176180)

 

Abstract

Traditional eddy detection methods based on SAR data require manually setting thresholds and feature parameters, which makes the process complex and difficult to automate. Existing deep learning models also suffer from high false negatives and false positives during detection, failing to meet the accuracy and efficiency requirements of eddy detection. To address these issues, this paper proposes an improved model based on YOLOv8, namely EddyDetNet, to overcome the above limitations and enhance both detection accuracy and efficiency. The model introduces an Adaptive Feature Compression Module (AFCM) in the Backbone and Neck to achieve lightweight and efficient feature extraction, and uses a Multi-scale Feature Spatial Pyramid Module (MFSP) to enhance the effect of multi-scale feature fusion. By optimizing the Neck structure and adding a small-object detection head in the Head part, the model improves the detection accuracy of eddies at different scales. Experimental results show that EddyDetNet outperforms YOLOv8 by 2.4%, 3.2%, and 5.5% in precision (P), recall (R), and mean Average Precision (mAP), respectively, while reducing the parameter size and computational complexity by 38.1% and 15.8%. Compared to YOLOv8, EddyDetNet reduces computational complexity and parameter size while maintaining high detection accuracy, making it suitable for eddy detection tasks in multi-target and complex background scenarios.

Cite this article

LIU Tailong XIE Tao LI Jian WANG Chao ZHANG Xuehong . Deep Learning-Based Automatic Detection Model for Ocean Eddy in SAR Images[J]. Journal of Tropical Oceanography, 0 : 1 . DOI: 10.11978/2024242

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